from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
from pybaseball import batting_stats
from pybaseball import pitching_stats
from pybaseball import lahman
lahman.download_lahman()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook
import seaborn as sns

# All box score pitching stats with WAR from 2018-2022
pitching_data = pitching_stats(2018,2022)
pitcher_WAR_data = pitching_data[['Season', 'Name', 'Team', 'Age', 'W', 'L', 'WAR', 'ERA', 'G', 'GS', 'CG', 'ShO', 'SV', 'BS', 'IP', 'H', 'R', 'ER', 'HR', 'BB', 'HBP', 'SO']]
pitcher_WAR_data

	Season	Name	Team	Age	W	L	WAR	ERA	G	GS	...	SV	BS	IP	H	R	ER	HR	BB	HBP	SO
1	2018	Jacob deGrom	NYM	30	10	9	9.0	1.70	32	32	...	0	0	217.0	152	48	41	10	46	5	269
23	2019	Gerrit Cole	HOU	28	20	5	7.5	2.50	33	33	...	0	0	212.1	142	66	59	29	48	3	326
25	2018	Max Scherzer	WSN	33	18	7	7.5	2.53	33	33	...	0	0	220.2	150	66	62	23	51	12	300
18	2021	Corbin Burnes	MIL	26	11	5	7.5	2.43	28	28	...	0	0	167.0	123	47	45	7	34	6	234
37	2021	Zack Wheeler	PHI	31	14	10	7.2	2.78	32	32	...	0	0	213.1	169	72	66	16	46	8	247
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
173	2022	Marco Gonzales	SEA	30	10	15	0.1	4.13	32	32	...	0	0	183.0	194	97	84	30	50	7	103
230	2021	Jordan Lyles	TEX	30	10	13	0.1	5.15	32	30	...	0	0	180.0	194	104	103	38	56	7	146
241	2018	Lucas Giolito	CHW	23	10	13	0.1	6.13	32	32	...	0	0	173.1	166	123	118	27	90	15	125
206	2020	Alec Mills	CHC	28	5	5	0.1	4.48	11	11	...	0	0	62.1	53	31	31	13	19	2	46
242	2020	Matthew Boyd	DET	29	3	7	-0.1	6.71	12	12	...	0	0	60.1	67	46	45	15	22	5	60

243 rows × 22 columns

# Creating separate pitching DataFrames to represent different types of stats
pitcher_wins_saves = pitcher_WAR_data[['WAR', 'W', 'L', 'SV', 'BS']]
pitcher_games_innings = pitcher_WAR_data[['WAR', 'G', 'GS', 'CG', 'ShO', 'IP']]
pitcher_era = pitcher_WAR_data[['WAR', 'H', 'HR', 'R', 'ER', 'ERA']]
pitcher_strikeouts = pitcher_WAR_data[['WAR', 'SO', 'BB', 'HBP']]

# Pitcher_wins_saves matrix plot
sns.pairplot(pitcher_wins_saves, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Wins are most correlated in this group

<seaborn.axisgrid.PairGrid at 0x7f7ac4e203a0>

# Pitcher_games_innings matrix plot
sns.pairplot(pitcher_games_innings, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Games, Games Started, and Innings Pitched are most correlated in this group

<seaborn.axisgrid.PairGrid at 0x7f7a6408c040>

# Pitcher_era matrix plot
sns.pairplot(pitcher_era, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Hits are most correlated in this group, but are not a clear positive correlation

<seaborn.axisgrid.PairGrid at 0x7f7a64081d30>

# Pitcher_strikeouts matrix plot
sns.pairplot(pitcher_strikeouts, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Strikeouts are most correlated in this group

<seaborn.axisgrid.PairGrid at 0x7f7a6807c9d0>

# New DataFrame with the most correlated stats to pitching WAR
pitcher_WAR_corr = pitcher_WAR_data[['Name', 'WAR', 'W', 'G', 'GS', 'IP', 'SO']]
pitcher_WAR_corr

	Name	WAR	W	G	GS	IP	SO
1	Jacob deGrom	9.0	10	32	32	217.0	269
23	Gerrit Cole	7.5	20	33	33	212.1	326
25	Max Scherzer	7.5	18	33	33	220.2	300
18	Corbin Burnes	7.5	11	28	28	167.0	234
37	Zack Wheeler	7.2	14	32	32	213.1	247
...	...	...	...	...	...	...	...
173	Marco Gonzales	0.1	10	32	32	183.0	103
230	Jordan Lyles	0.1	10	32	30	180.0	146
241	Lucas Giolito	0.1	10	32	32	173.1	125
206	Alec Mills	0.1	5	11	11	62.1	46
242	Matthew Boyd	-0.1	3	12	12	60.1	60

243 rows × 7 columns

# Matrix plot of most correlated stats to pitching WAR
sns.pairplot(pitcher_WAR_corr, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Visually it seems as if Wins and Strikeouts are most positively correlated

<seaborn.axisgrid.PairGrid at 0x7f7a7bbe8eb0>

# Correlation matrix to determine which stat is most positively correlated
pitcher_WAR_corr.corr()

# Strikeouts is 77.83% correlated to WAR which is the most among box score statistics

	WAR	W	G	GS	IP	SO
WAR	1.000000	0.570973	0.393841	0.408317	0.529834	0.778301
W	0.570973	1.000000	0.698538	0.705301	0.750548	0.677330
G	0.393841	0.698538	1.000000	0.995517	0.966460	0.728666
GS	0.408317	0.705301	0.995517	1.000000	0.970789	0.738871
IP	0.529834	0.750548	0.966460	0.970789	1.000000	0.800208
SO	0.778301	0.677330	0.728666	0.738871	0.800208	1.000000

# All box score batting stats with WAR from 2018-2022
batting_data = batting_stats(2018,2022)
batter_WAR_data = batting_data[['Season', 'Name', 'Team', 'Age', 'WAR', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'SO', 'HBP', 'SB', 'AVG', 'OBP', 'SLG', 'OPS']]
batter_WAR_data

	Season	Name	Team	Age	WAR	G	AB	PA	H	1B	...	R	RBI	BB	SO	HBP	SB	AVG	OBP	SLG	OPS
1	2022	Aaron Judge	NYY	30	11.4	157	570	696	177	87	...	133	131	111	175	6	16	0.311	0.425	0.686	1.111
3	2018	Mookie Betts	BOS	25	10.5	136	520	614	180	96	...	129	80	81	91	8	30	0.346	0.438	0.640	1.078
4	2018	Mike Trout	LAA	26	9.5	140	471	608	147	80	...	101	79	122	124	10	24	0.312	0.460	0.628	1.088
7	2019	Mike Trout	LAA	27	8.4	134	470	600	137	63	...	110	104	110	120	16	11	0.291	0.438	0.645	1.083
16	2019	Alex Bregman	HOU	25	8.1	156	554	690	164	84	...	122	112	119	83	9	5	0.296	0.423	0.592	1.015
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
627	2021	Hunter Dozier	KCR	29	-1.0	144	487	543	105	56	...	55	54	43	154	7	5	0.216	0.285	0.394	0.680
634	2020	J.D. Martinez	BOS	32	-1.0	54	211	237	45	22	...	22	27	22	59	2	1	0.213	0.291	0.389	0.680
668	2020	Jonathan Villar	- - -	29	-1.1	52	185	207	43	36	...	13	15	19	54	0	16	0.232	0.301	0.292	0.593
650	2018	Victor Martinez	DET	39	-1.7	133	467	508	117	87	...	32	54	32	49	2	0	0.251	0.297	0.353	0.651
678	2018	Chris Davis	BAL	32	-2.6	128	470	522	79	51	...	40	49	41	192	7	2	0.168	0.243	0.296	0.539

679 rows × 23 columns

# Creating separate batting DataFrames to represent different types of stats
batter_bip = batter_WAR_data[['WAR', 'H', '1B', '2B', '3B', 'HR']]
batter_games = batter_WAR_data[['WAR', 'G', 'AB', 'PA', 'R', 'RBI']]
batter_averages = batter_WAR_data[['WAR', 'AVG', 'OBP', 'SLG', 'OPS']]
batter_non_bip = batter_WAR_data[['WAR', 'SO', 'BB', 'HBP', 'SB']]

# Batter_bip matrix plot
sns.pairplot(batter_bip, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Hits and Homeruns are most correlated in this group

<seaborn.axisgrid.PairGrid at 0x7f7a7bc30e80>

# Batter_games matrix plot
sns.pairplot(batter_games, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Most of these stats are strongly correlated to WAR, but it seems like Runs and RBIs are slightly more correlated to WAR

<seaborn.axisgrid.PairGrid at 0x7f7ace5a1b50>

# Batter_averages matrix plot
sns.pairplot(batter_averages, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# OBP and OPS are most correlated in this group

<seaborn.axisgrid.PairGrid at 0x7f7ad358d0d0>

# Batter_non_bip matrix plot
sns.pairplot(batter_non_bip, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Walks are most correlated in this group

<seaborn.axisgrid.PairGrid at 0x7f7a59997d30>

# New DataFrame with the most correlated stats to batting WAR
batter_WAR_corr = batter_WAR_data[['Name', 'WAR', 'H', 'HR', 'R', 'RBI', 'BB', 'OBP', 'OPS']]
batter_WAR_corr

	Name	WAR	H	HR	R	RBI	BB	OBP	OPS
1	Aaron Judge	11.4	177	62	133	131	111	0.425	1.111
3	Mookie Betts	10.5	180	32	129	80	81	0.438	1.078
4	Mike Trout	9.5	147	39	101	79	122	0.460	1.088
7	Mike Trout	8.4	137	45	110	104	110	0.438	1.083
16	Alex Bregman	8.1	164	41	122	112	119	0.423	1.015
...	...	...	...	...	...	...	...	...	...
627	Hunter Dozier	-1.0	105	16	55	54	43	0.285	0.680
634	J.D. Martinez	-1.0	45	7	22	27	22	0.291	0.680
668	Jonathan Villar	-1.1	43	2	13	15	19	0.301	0.593
650	Victor Martinez	-1.7	117	9	32	54	32	0.297	0.651
678	Chris Davis	-2.6	79	16	40	49	41	0.243	0.539

679 rows × 9 columns

# Matrix plot of most correlated stats to batting WAR
sns.pairplot(batter_WAR_corr, diag_kind = 'kde', kind='reg', plot_kws={'line_kws':{'color':'red'}, 
                                                                          'scatter_kws':{'alpha':0.2}})
# Visually it seems as if Runs and OPS are most positively correlated

<seaborn.axisgrid.PairGrid at 0x7f7a5e7c9b20>

# Correlation matrix to determine which stat is most positively correlated
batter_WAR_corr.corr()

# Runs is 71.44% correlated to WAR which is the most among box score statistics

	WAR	H	HR	R	RBI	BB	OBP	OPS
WAR	1.000000	0.583690	0.583353	0.714397	0.577583	0.529210	0.590827	0.654426
H	0.583690	1.000000	0.537161	0.879306	0.771576	0.521174	0.211779	0.233489
HR	0.583353	0.537161	1.000000	0.713834	0.863469	0.592449	0.252416	0.585151
R	0.714397	0.879306	0.713834	1.000000	0.798322	0.697615	0.330426	0.401709
RBI	0.577583	0.771576	0.863469	0.798322	1.000000	0.596534	0.214559	0.443957
BB	0.529210	0.521174	0.592449	0.697615	0.596534	1.000000	0.487114	0.355991
OBP	0.590827	0.211779	0.252416	0.330426	0.214559	0.487114	1.000000	0.821281
OPS	0.654426	0.233489	0.585151	0.401709	0.443957	0.355991	0.821281	1.000000

# Pitching data from 1871-2000
all_pitching_data = pitching_stats(1871, 2000)
all_pitcher_WAR_data = all_pitching_data[['Name', 'WAR', 'W', 'L', 'ERA', 'G', 'GS', 'CG', 'ShO', 'SV', 'BS', 'IP', 'H', 'R', 'ER', 'HR', 'BB', 'HBP', 'SO']]
all_pitcher_WAR_data

	Name	WAR	W	L	ERA	G	GS	CG	ShO	SV	BS	IP	H	R	ER	HR	BB	HBP	SO
343	Pedro Martinez	11.6	23	4	2.07	31	29	5	1	0	NaN	213.1	160	56	49	9.0	37	9.0	313
79	Silver King	11.1	45	21	1.64	66	65	64	6	0	NaN	585.2	437	205	107	6.0	76	0.0	258
261	Steve Carlton	11.1	27	10	1.97	41	41	30	8	0	NaN	346.1	257	84	76	17.0	87	1.0	310
1626	John Clarkson	10.9	49	19	2.73	73	72	68	8	1	NaN	620.0	589	280	188	16.0	203	17.0	284
918	Toad Ramsey	10.9	38	27	2.45	67	67	66	3	0	NaN	588.2	447	297	160	3.0	207	12.0	499
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8515	Phil Ortega	-1.9	12	15	5.11	35	29	4	2	0	NaN	179.2	176	107	102	33.0	97	5.0	88
8957	George Mitchell	-1.9	4	6	6.35	17	13	7	1	0	NaN	90.2	107	91	64	NaN	55	2.0	36
8944	Hulon Stamps	-1.9	0	13	6.24	20	10	4	0	0	NaN	115.1	134	98	80	NaN	36	2.0	37
9016	Charlie Beverly	-2.0	5	12	7.88	29	16	6	0	0	NaN	128.0	175	120	112	NaN	49	3.0	44
8884	Leo Birdine	-2.1	4	13	5.91	28	19	9	0	0	NaN	147.2	207	134	97	NaN	45	0.0	43

9026 rows × 19 columns

# Correlation matrix for pitching WAR
all_pitcher_WAR_data.corr()

# Strikeouts  is still most correlated at 69.71% with Wins close behind at 68.09%

	WAR	W	L	ERA	G	GS	CG	ShO	SV	BS	IP	H	R	ER	HR	BB	HBP	SO
WAR	1.000000	0.680884	0.188978	-0.489145	0.454505	0.592117	0.458850	0.511301	0.048175	NaN	0.635193	0.471853	0.233107	0.221718	0.009902	0.171095	0.062444	0.697081
W	0.680884	1.000000	0.214302	-0.472914	0.643256	0.751826	0.682246	0.558713	0.067155	NaN	0.835361	0.678405	0.453184	0.403014	0.054353	0.378147	0.197747	0.557432
L	0.188978	0.214302	1.000000	0.054477	0.575017	0.619989	0.528053	0.109820	-0.014359	NaN	0.642440	0.719605	0.731163	0.693442	0.060153	0.450979	0.235149	0.253705
ERA	-0.489145	-0.472914	0.054477	1.000000	-0.217759	-0.231499	-0.363430	-0.531039	-0.117049	NaN	-0.370945	-0.090789	0.172277	0.371505	0.325090	0.025825	-0.109328	-0.325193
G	0.454505	0.643256	0.575017	-0.217759	1.000000	0.721791	0.479947	0.345463	0.358795	NaN	0.794706	0.739004	0.575269	0.613990	0.170259	0.515924	0.201783	0.477796
GS	0.592117	0.751826	0.619989	-0.231499	0.721791	1.000000	0.575404	0.418335	-0.138486	NaN	0.901411	0.825226	0.662531	0.699739	0.323820	0.537343	0.246861	0.647579
CG	0.458850	0.682246	0.528053	-0.363430	0.479947	0.575404	1.000000	0.434180	0.030041	NaN	0.817015	0.769534	0.694974	0.458658	-0.314691	0.338355	0.264179	0.261231
ShO	0.511301	0.558713	0.109820	-0.531039	0.345463	0.418335	0.434180	1.000000	0.052818	NaN	0.485313	0.273560	0.044042	-0.002449	-0.089205	0.148490	0.105725	0.411012
SV	0.048175	0.067155	-0.014359	-0.117049	0.358795	-0.138486	0.030041	0.052818	1.000000	NaN	0.033504	0.010808	-0.057611	-0.054429	-0.129854	0.017209	-0.026495	-0.045234
BS	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
IP	0.635193	0.835361	0.642440	-0.370945	0.794706	0.901411	0.817015	0.485313	0.033504	NaN	1.000000	0.910838	0.741548	0.663719	0.054358	0.521878	0.289317	0.575344
H	0.471853	0.678405	0.719605	-0.090789	0.739004	0.825226	0.769534	0.273560	0.010808	NaN	0.910838	1.000000	0.892601	0.823518	0.070604	0.446196	0.254103	0.340185
R	0.233107	0.453184	0.731163	0.172277	0.575269	0.662531	0.694974	0.044042	-0.057611	NaN	0.741548	0.892601	1.000000	0.873801	0.058364	0.489638	0.235737	0.216607
ER	0.221718	0.403014	0.693442	0.371505	0.613990	0.699739	0.458658	-0.002449	-0.054429	NaN	0.663719	0.823518	0.873801	1.000000	0.364077	0.586438	0.189522	0.288696
HR	0.009902	0.054353	0.060153	0.325090	0.170259	0.323820	-0.314691	-0.089205	-0.129854	NaN	0.054358	0.070604	0.058364	0.364077	1.000000	0.131962	-0.098878	0.346243
BB	0.171095	0.378147	0.450979	0.025825	0.515924	0.537343	0.338355	0.148490	0.017209	NaN	0.521878	0.446196	0.489638	0.586438	0.131962	1.000000	0.204844	0.444592
HBP	0.062444	0.197747	0.235149	-0.109328	0.201783	0.246861	0.264179	0.105725	-0.026495	NaN	0.289317	0.254103	0.235737	0.189522	-0.098878	0.204844	1.000000	0.180665
SO	0.697081	0.557432	0.253705	-0.325193	0.477796	0.647579	0.261231	0.411012	-0.045234	NaN	0.575344	0.340185	0.216607	0.288696	0.346243	0.444592	0.180665	1.000000

# Batting data from 1871-2000
all_batting_data = batting_stats(1871, 2000)
all_batter_WAR_data = all_batting_data[['Name', 'WAR', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'SO', 'HBP', 'SB', 'AVG', 'OBP', 'SLG', 'OPS']]
all_batter_WAR_data

	Name	WAR	G	AB	PA	H	1B	2B	3B	HR	R	RBI	BB	SO	HBP	SB	AVG	OBP	SLG	OPS
4	Babe Ruth	14.7	152	522	699	205	106	45	13	41	151	131.0	170	93.0	4.0	17.0	0.393	0.545	0.764	1.309
3	Babe Ruth	13.8	152	540	693	204	85	44	16	59	177	171.0	145	81.0	4.0	17.0	0.378	0.512	0.846	1.359
1	Babe Ruth	13.2	142	457	615	172	73	36	9	54	158	137.0	150	80.0	3.0	14.0	0.376	0.533	0.849	1.382
13	Babe Ruth	13.0	151	540	691	192	95	29	8	60	158	164.0	137	89.0	0.0	7.0	0.356	0.486	0.772	1.258
15	Lou Gehrig	12.4	155	584	717	218	101	52	18	47	149	175.0	109	84.0	3.0	10.0	0.373	0.474	0.765	1.240
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10102	Jose Guillen	-3.1	143	498	526	133	94	20	5	14	58	70.0	17	88.0	8.0	1.0	0.267	0.300	0.412	0.712
12611	Jim Levey	-3.2	139	498	540	104	78	19	2	5	53	38.0	35	83.0	2.0	13.0	0.209	0.264	0.285	0.549
12987	Jim Lillie	-3.2	114	416	427	73	64	9	0	0	37	22.0	11	80.0	NaN	13.0	0.175	0.197	0.197	0.394
11141	Tommy Thevenow	-3.5	156	573	624	164	142	21	1	0	57	78.0	23	26.0	2.0	1.0	0.286	0.316	0.326	0.642
12921	Jim Levey	-3.9	141	529	567	103	87	10	4	2	43	36.0	26	68.0	3.0	4.0	0.195	0.237	0.240	0.477

13018 rows × 20 columns

# Correlation matrix for batting WAR
all_batter_WAR_data.corr()

# Runs is less correlated than OBP, SLG, and OPS which is 73.16% correlated to WAR

	WAR	G	AB	PA	H	1B	2B	3B	HR	R	RBI	BB	SO	HBP	SB	AVG	OBP	SLG	OPS
WAR	1.000000	0.328752	0.340913	0.410001	0.568785	0.363361	0.504153	0.288557	0.480472	0.642181	0.563383	0.533558	0.143932	0.152366	0.175348	0.585404	0.688472	0.677858	0.731626
G	0.328752	1.000000	0.943937	0.958368	0.789931	0.696301	0.577399	0.146838	0.382357	0.515991	0.509385	0.539334	0.463089	0.086276	0.094239	0.001054	0.162660	0.173969	0.182505
AB	0.340913	0.943937	1.000000	0.981605	0.886902	0.812467	0.627708	0.248027	0.333753	0.629421	0.522622	0.434979	0.345067	0.081291	0.177070	0.100973	0.146715	0.190498	0.187950
PA	0.410001	0.958368	0.981605	1.000000	0.876536	0.784715	0.630875	0.229792	0.374241	0.671557	0.545163	0.588830	0.378329	0.125395	0.181415	0.112539	0.261281	0.230156	0.258880
H	0.568785	0.789931	0.886902	0.876536	1.000000	0.898493	0.740830	0.366035	0.357203	0.762388	0.640078	0.423448	0.143669	0.090433	0.193798	0.527062	0.469437	0.475301	0.508004
1B	0.363361	0.696301	0.812467	0.784715	0.898493	1.000000	0.473127	0.339363	-0.013046	0.606432	0.338032	0.266258	-0.109893	0.071899	0.301512	0.445713	0.332259	0.130040	0.216172
2B	0.504153	0.577399	0.627708	0.630875	0.740830	0.473127	1.000000	0.183399	0.399804	0.581643	0.616735	0.369833	0.193815	0.049858	-0.010550	0.439485	0.420234	0.561413	0.549206
3B	0.288557	0.146838	0.248027	0.229792	0.366035	0.339363	0.183399	1.000000	-0.173315	0.390303	0.175712	0.023702	-0.225460	0.080641	0.342323	0.322383	0.218664	0.196330	0.219214
HR	0.480472	0.382357	0.333753	0.374241	0.357203	-0.013046	0.399804	-0.173315	1.000000	0.415959	0.733807	0.451962	0.615762	0.033879	-0.242162	0.164018	0.316744	0.746456	0.638574
R	0.642181	0.515991	0.629421	0.671557	0.762388	0.606432	0.581643	0.390303	0.415959	1.000000	0.587023	0.574103	0.162668	0.208391	0.376052	0.485132	0.589065	0.540849	0.598790
RBI	0.563383	0.509385	0.522622	0.545163	0.640078	0.338032	0.616735	0.175712	0.733807	0.587023	1.000000	0.427977	0.334853	0.092082	-0.076260	0.421578	0.465175	0.737306	0.688968
BB	0.533558	0.539334	0.434979	0.588830	0.423448	0.266258	0.369833	0.023702	0.451962	0.574103	0.427977	1.000000	0.337431	0.072172	0.078611	0.126667	0.642563	0.357302	0.491558
SO	0.143932	0.463089	0.345067	0.378329	0.143669	-0.109893	0.193815	-0.225460	0.615762	0.162668	0.334853	0.337431	1.000000	0.072254	-0.037431	-0.218278	0.022248	0.302212	0.219681
HBP	0.152366	0.086276	0.081291	0.125395	0.090433	0.071899	0.049858	0.080641	0.033879	0.208391	0.092082	0.072172	0.072254	1.000000	0.198203	0.043140	0.154777	0.045905	0.089764
SB	0.175348	0.094239	0.177070	0.181415	0.193798	0.301512	-0.010550	0.342323	-0.242162	0.376052	-0.076260	0.078611	-0.037431	0.198203	1.000000	0.091444	0.105771	-0.115325	-0.042052
AVG	0.585404	0.001054	0.100973	0.112539	0.527062	0.445713	0.439485	0.322383	0.164018	0.485132	0.421578	0.126667	-0.218278	0.043140	0.091444	1.000000	0.792378	0.717791	0.798706
OBP	0.688472	0.162660	0.146715	0.261281	0.469437	0.332259	0.420234	0.218664	0.316744	0.589065	0.465175	0.642563	0.022248	0.154777	0.105771	0.792378	1.000000	0.710707	0.872392
SLG	0.677858	0.173969	0.190498	0.230156	0.475301	0.130040	0.561413	0.196330	0.746456	0.540849	0.737306	0.357302	0.302212	0.045905	-0.115325	0.717791	0.710707	1.000000	0.963872
OPS	0.731626	0.182505	0.187950	0.258880	0.508004	0.216172	0.549206	0.219214	0.638574	0.598790	0.688968	0.491558	0.219681	0.089764	-0.042052	0.798706	0.872392	0.963872	1.000000